Skills in Machine Learning are definitely one of the hottest in the tech world right now with applications ranging from self-driving cars to human-like robots.
While Python certainly dominates in popularity with 57% of data scientists and machine learning developers using it and 33% preferring it over other languages for developments, Java does have uses also particularly for network security, cyber-attacks and fraud detection.
So how can developers leverage their Java skills to get into machine learning? We suggest the following steps:
1. Understanding the fundamentals
You have to understand the statistical and theory-based foundation of machine learning in order to properly implement it. This is true no matter what languages you are proficient in. A good place to start are Google’s courses on deep learning and reading up on how machine learning algorithms work.
If you need a maths, stats and probability refresher, KhanAcademy has helpful resources on the linear algebra, probability & statistics and calculus that you need on how to understand what you’re doing with machine learning models.
2. Practice implementing machine learning models with Java workbenches
You need to have an environment that has machine learning frameworks with a GUI (graphical user interface) that allows you to interact directly with machine learning algorithms in an intuitive fashion. Weka, for example has a series of implementations of machine learning that are pre-written in Java.
3. Work with different Java machine learning libraries
Once you have worked in a Java environment-friendly to machine learning, you should then go on and practice with different Java frameworks, particularly ones that are specifically written to implement certain machine learning algorithms. Make sure you get some practice in with the libraries best suited to your machine learning needs.
4. Deploy your first projects
At the end of the day, machine learning is about taking ML models and applying them to data in a way that is scalable and performant. You will want to take the theory that you have learned and apply them to different problems.
If you need datasets to play with, take a look at Kaggle. Not only does the platform offer you the opportunity to compete and demonstrate your machine learning skills, but it also offers datasets that are rated by their popularity and contextualized with different projects that other people have built on them.
If you can’t find what you’re looking for on Kaggle, this Github repository has links to all sorts of awesome public data resources you can experiment with.
5. Advance your career with your new skills
Finally, once you have built a few projects and worked with different machine learning frameworks, you are ready to start networking with ML engineers and applying your new skills in your current job or perhaps in a new job as a machine learning engineer.
So whether your a software engineer looking add machine learning into their toolkit or somebody who wants to work as a machine learning engineer full-time, follow these steps and potentially change your life.